SBIR Phase I: Monitoring Desalination Membrane Fouling via Real-time Integrated Surface Imaging and Advanced Machine Learning

SBIR 第一阶段：通过实时集成表面成像和先进机器学习监测海水淡化膜污垢

• 批准号: 2019726
• 负责人: Greg Pawin
• 金额: $ 25.54万
• 依托单位: Noria Water Technologies, Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019726&HistoricalAwards=false
• 关键词: SBIR; Phase; Monitoring; Desalination; Membrane

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to enable monitoring and decision support for membrane-based water treatment and desalination processes in the municipal water reuse, agriculture and industrial sectors. The proposed technology will integrate of machine learning (ML) and artificial intelligence (AI) decision support technology with hardware for direct and real-time visualization of process element conditions and performance process monitoring to reduce operational risks and costs. Moreover, the proposed platform will enable more efficient and lower-cost water production from non-traditional and underutilized source waters, such as municipal and industrial wastewater, high salinity and/or contaminated groundwater, and seawater. This Small Business Innovation Research (SBIR) Phase I project will advance translation of a real-time detection, characterization and forecasting of membrane fouling and scaling of reverse osmosis (RO) and nanofiltration (NF) membranes in water treatment and desalination plants. ML and AI approaches will integrate direct and real-time membrane surface foulant imaging with operating performance data for real-time analysis and forecasting of plant fouling and its impact on plant performance. The research will explore relationships between foulant/scalant characteristics and plant process conditions for a Dynamic Bayesian Network model to inform a decision support system for timely fouling/scaling mitigation in RO/NF plants.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响将是为市政水回用、农业和工业部门的膜水处理和海水淡化过程提供监测和决策支持。拟议的技术将机器学习（ML）和人工智能（AI）决策支持技术与硬件相结合，以直接实时可视化过程要素条件和性能过程监控，以降低运营风险和成本。 此外，拟议的平台将使非传统和未充分利用的水源（例如市政和工业废水、高盐度和/或受污染的地下水以及海水）更高效、更低成本地生产水。该小型企业创新研究 (SBIR) 第一阶段项目将推进水处理和海水淡化厂中反渗透 (RO) 和纳滤 (NF) 膜的膜污染和结垢的实时检测、表征和预测的转化。机器学习和人工智能方法将直接、实时的膜表面污垢成像与运行性能数据相结合，以实时分析和预测工厂污垢及其对工厂性能的影响。该研究将探索动态贝叶斯网络模型的污垢/结垢特征与工厂工艺条件之间的关系，为决策支持系统提供信息，以便及时减少 RO/NF 工厂的污垢/结垢。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。